MCP Context Forge allows developers to define multiple channels such as text, code, embeddings, and custom metadata, orchestrating them into cohesive context windows for AI agents. Through its pipeline architecture, it automates segmentation of source data, enriches it with annotations, and merges channels based on configurable strategies like priority weighting or dynamic pruning. The framework supports adaptive context length management, retrieval-augmented generation, and seamless integration with IBM Watson and third-party LLMs, ensuring AI agents access relevant, concise, and up-to-date context. This improves performance in tasks like conversational AI, document Q&A, and automated summarization.

Modal is a next-generation serverless platform designed for AI, data science, and machine learning teams. It facilitates the running of generative AI models, large-scale batch jobs, job queues, and much more. With Modal, developers can bring their own code, run it in the cloud without concerning themselves with infrastructure, and scale production workloads efficiently using thousands of CPUs and GPUs. Modal provides an effortless setup and integration for a high-performance computational environment, helping teams to innovate and develop faster with lower costs.